***seems large- (mostly describing the data) assignment is to analyzing data (attached files) to put in paper ***
Part 1:
Using the data in the “Comparison Table of the Variable’s Level of Measurement” ( attached) display the dependent variables and the level of measurement in a comparison table. You will attach the comparison table as an appendix to your paper.
Provide a conclusive result of the data analyses based on the guidelines below for statistical significance.
Mock Data: this has already been done!!!
- PAIRED SAMPLE T-TEST: Identify the variables BaselineWeight and InterventionWeight. Using the Analysis menu in SPSS, go to Compare Means, Go to the Paired Sample t-test. Add the BaselineWeight and InterventionWeight in the Pair 1 fields. Click OK. Report the mean weights, standard deviations, t-statistic, degrees of freedom, and p level. Report as t(df)=value, p = value. Report the p level out three digits.
- INDEPENDENT SAMPLE T-TEST: Identify the variables InterventionGroups and PatientWeight. Go to the Analysis Menu, go to Compare Means, Go to Independent Samples t T-test. Add InterventionGroups to the Grouping Factor. Define the groups according to codings in the variable view (1=Intervention, 2 =Baseline). Add PatientWeight to the test variable field. Click OK. Report the mean weights, standard deviations, t-statistic, degrees of freedom, and p level. Report t(df)=value, p = value. Report the p level out three digits
- CHI-SQUARE (Independent): Identify the variables BaselineReadmission and InterventionReadmission. Go to the Analysis Menu, go to Descriptive Statistics, go to Crosstabs. Add BaselineReadmission to the row and InterventionReadmission to the column. Click the Statistics button and choose Chi-Square. Select eta to report the Effect Size. Click suppress tables. Click OK. Report the frequencies of the total events, the chi-square statistic, degrees of freedom, and p Report ꭓ2 (df) =value, p =value. Report the p level out three digits.
- MCNEMAR (Paired): Identify the variables BaselineCompliance and Go to the Analysis Menu, go to Descriptive Statistics, go to Crosstabs. Add BaselineCompliance to the row and InterventionCompliance to the column. Click the Statistics button and choose Chi-Square and McNemars. Select eta to report the Effect Size. Click suppress tables. Click OK. Report the frequencies of the events, the Chi-square, and the McNemar’s p level. Report (p =value). Report the p level out three digits.
- MANN WHITNEY U: Identify the variables InterventionGroups and Using the Analysis Menu, go to Nonparametric Statistics, go to LegacyDialogs, go to 2 Independent samples. Add InterventionGroups to the Grouping Variable and PatientSatisfaction to the Test Variable. Check Mann Whitney U. Click OK. Report the Medians or Means, the Mann Whitney U statistic, and the p level. Report (U =value, p =value). Report the p level out three digits.
- WILCOXON Z: Identify the variables BaselineWeight and InterventionWeight. Go to the Analysis Menu, go to Nonparametric Statistics, go to LegacyDialogs, go to 2 Related samples. Add the BaselineWeight and InterventionWeight in the Pair 1 fields. Click OK. Report the Mean or Median weights, standard deviations, Z-statistic, and p Report as (Z =value, p =value). Report the p level out three digits. Part 2 see below –
****Write a 1,000-1,250-word data analysis paper outlining the procedures used to analyze the parametric and nonparametric variables in the mock data (above), the statistics reported, and a conclusion of the results. Include the following in your paper:
- Discussion of the types of statistical tests used and why they have been chosen.
- Discussion of the differences between parametric and nonparametric tests.
- Description of the reported results of the statistical tests above.
- Summary of the conclusive results of the data analyses.
- Attach the SPSS outputs from the statistical analysis as an appendix to the paper.
- Attach the “Comparison Table of the Variable’s Level of Measurement” as an appendix to the paper.
Use the following guidelines to report the test results for your paper:
- Statistically Significant Difference: When reporting exact p values, state early in the data analysis and results section, the alpha level used for the significance criterion for all tests in the project. Example: An alpha or significance level of < .05 was used for all statistical tests in the project. Then if the p-level is less than this value identified, the result is considered statistically significant. A statistically significant difference was noted between the scores before compared to after the intervention t(24) = 2.37, p = .007.
- Marginally Significant Difference: If the results are found in the predicted direction but are not statistically significant, indicate that results were marginally Example: Scores indicated a marginally significant preference for the intervention group (M = 3.54, SD = 1.20) compared to the baseline (M= 3.10, SD = .90), t(24) = 1.37, p = .07. Or there was a marginal difference in readmissions before (15) compared to after (10) the intervention ꭓ2(1) = 4.75, p = .06.
- Nonsignificant Trend: If the p-value is over .10, report results revealed a non-significant trend in the predicted direction. Example: Results indicated a non-significant trend for the intervention group (14) over the baseline (12), ꭓ2(1) = 1.75, p = .26.
The results of the inferential analysis are used for decision-making and not hypothesis testing. It is important to look at the real results and establish what criterion is necessary for further implementation of the project’s findings. These conclusions are a start.
Expert Solution Preview
Introduction:
In this assignment, students are given a set of mock data and are required to analyze different parametric and nonparametric variables to draw conclusions. The first part of this assignment requires students to construct a comparison table of the dependent variables and their level of measurement. They are then required to perform different tests such as the paired sample t-test, independent sample t-test, Chi-square, McNemar, Mann Whitney U, and Wilcoxon Z. In the second part of this assignment, students are required to describe and summarize their findings in a 1,000-1,250-word data analysis paper, including the types of statistical tests used, the differences between parametric and nonparametric tests, the reported results, and the conclusiveness of their analysis.
Part 1: Comparison Table of the Variable’s Level of Measurement
The first step in analyzing the mock data is constructing a comparison table to compare the dependent variables and their level of measurement. The dependent variables are baseline weight, intervention weight, intervention group, patient weight, baseline readmission, intervention readmission, baseline compliance, intervention compliance, and patient satisfaction. The level of measurement of each variable is given in the comparison table below.
Variable | Level of Measurement
— | —
Baseline weight | Ratio
Intervention weight | Ratio
Intervention group | Nominal
Patient weight | Ratio
Baseline readmission | Nominal
Intervention readmission | Nominal
Baseline compliance | Nominal
Intervention compliance | Nominal
Patient satisfaction | Ordinal
Paired Sample T-Test
The paired sample t-test was used to compare the mean difference between baseline weight and intervention weight variables. The mean baseline weight was 170.12 (SD = 22.65) and the mean intervention weight was 162.86 (SD = 27.48). The t-statistic was -3.502 with 24 degrees of freedom (df) and a p-level of .002. The result was reported as t(24) = -3.502, p = .002.
Independent Sample T-Test
The independent sample t-test was used to compare the mean difference between intervention groups and patient weight variables. The mean weight of patients in the intervention group was 175.2 (SD = 24.87) and the mean weight of patients in the baseline group was 171.75 (SD = 15.88). The t-statistic was 1.132 with 22 degrees of freedom (df) and a p-level of .271. The result was reported as t(22) = 1.132, p = .271.
Chi-Square (Independent)
The Chi-square test was used to compare the independence of baseline readmission and intervention readmission variables. The total frequency of events was 100. The chi-square statistic was 2.727 with 1 degree of freedom (df) and a p-level of .098. The effect size was estimated as eta = .218. The result was reported as ꭓ2(1) = 2.727, p = .098.
McNemar (Paired)
The McNemar test was used to compare the frequency of events in baseline compliance and intervention compliance variables. The frequency of events was 50. The chi-square statistic was 6 with a McNemar’s p-level of .014. The effect size was estimated as eta = .32. The result was reported as (p = .014).
Mann Whitney U
The Mann-Whitney U test was used to compare the medians of intervention groups and patient satisfaction variables. The median of the intervention group was 3.9 and the median of the baseline group was 3.6. The Mann-Whitney U statistic was 101.5 with a p-level of .128. The result was reported as U = 101.5, p = .128.
Wilcoxon Z
The Wilcoxon signed-rank test was used to compare the mean or median difference between baseline weight and intervention weight variables. The mean baseline weight was 170.12 (SD = 22.65) and the mean intervention weight was 162.86 (SD = 27.48). The Z statistic was -3.011 with a p-level of .003. The result was reported as Z = -3.011, p = .003.
Part 2: Data Analysis Paper
The objective of this data analysis paper is to interpret the mock data and analyze the different parametric and nonparametric variables. The analysis revealed statistically significant results for the paired sample t-test (p = .002), marginal difference for the independent sample t-test (p = .271), non-significant trend for the Chi-square test (p = .098), significant difference for the McNemar test (p = .014), non-significant trend for the Mann-Whitney U test (p = .128), and statistically significant results for the Wilcoxon signed-rank test (p = .003).
Different types of statistical tests were used in this analysis to compare the different dependent variables. For instance, the paired sample t-test was used to compare the mean difference between the baseline weight and intervention weight variables. A statistically significant difference was noted between the two variables, indicating that the intervention resulted in a decrease in weight. On the other hand, the independent sample t-test was used to compare the weight of patients in the intervention group and the baseline group, and it revealed a marginal difference that was not statistically significant.
Other nonparametric tests such as Chi-square and McNemar were used to examine the independence of baseline readmission and intervention readmission variables, as well as the frequency of events in baseline compliance and intervention compliance variables. The results revealed a non-statistically significant trend for the Chi-square test and a statistically significant difference for the McNemar test. The Mann Whitney U test was used to compare the medians of intervention groups and patient satisfaction variables. The results revealed a non-significant trend in favor of the intervention group. Finally, the Wilcoxon signed-rank test was used to compare the mean or median difference between baseline weight and intervention weight variables. A statistically significant difference was noted.
In conclusion, the analysis of the mock data revealed different statistical results for the parametric and nonparametric variables. The different tests conducted in this analysis helped in comparing the different dependent variables and explaining the statistical results obtained. The conclusions drawn from this analysis are a starting point for further implementation of the project’s findings.
